Three-dimensional high-precision maps are recognized in the industry and academia as the major development direction of the next-generation digital maps, which is a prerequisite for realizing the self-driving and assisted driving of automobiles, and provides a major foundation for the precise positioning and right decision of self-driving automobiles. The high-precision map is also an important strategic platform resource for analyzing the road utilization status and achieving intelligent transportation. The main issue in producing the three-dimensional high-precision map concentrates on the detection and generation of road lane information, i.e., using the three-dimensional digital map to precisely reconstruct road network lane information in the real world.
Currently, there are mainly two approaches for the detection and generation of vehicular lane lines. One is a manual approach, by comparing road images and point cloud information acquired by an industrial camera, the stitched point clouds are colored by utilizing the road images, and the road lane information such as vehicular lane lines on the colored point cloud are manually drawn and labelled. The other approach is to detect the vehicular lane lines in the images by using an automatic recognition method, whereby the vehicular lane lines are detected and filtered through band-pass filters and various priori rules.
The first approach to detect and generate vehicular lane lines has low efficiency and high labor cost. The drawing of three-dimensional lines such as the vehicular lane lines on the three-dimensional point cloud is difficult to interact, the drawn lines are tortuous, the manual operation has low efficiency. Because the point cloud has a low resolution, it is very likely to leave out vehicular lane lines during the drawing. However, the algorithm and solution applied in the second approach to detect and generate vehicular lane lines mainly serve the real-time vehicular lane line detection in automated driving, and remain mainly experimental, and the effectiveness and physical precision of the detection cannot meet the production requirements of the high-precision maps. Thus it can be seen that the existing vehicular lane line detection and generation methods need to be further improved.